- © 2005 American Society of Plant Biologists
Abstract
Patterns of synthesis and breakdown of carbon (C) and nitrogen (N) stores are relatively well known. But the role of mobilized stores as substrates for growth remains less clear. In this article, a novel approach to estimate C and N import into leaf growth zones was coupled with steady-state labeling of photosynthesis (13CO2/12CO2) and N uptake (15NO3−/14NO3−) and compartmental modeling of tracer fluxes. The contributions of current C assimilation/N uptake and mobilization from stores to the substrate pool supplying leaf growth were then quantified in plants of a C3 (Lolium perenne) and C4 grass (Paspalum dilatatum Poir.) manipulated thus to have contrasting C assimilation and N uptake rates. In all cases, leaf growth relied largely on photoassimilates delivered either directly after fixation or short-term storage (turnover rate = 1.6–3.3 d−1). Long-term C stores (turnover rate < 0.09 d−1) were generally of limited relevance. Hence, no link was found between the role of stores and C acquisition rate. Short-term (turnover rate = 0.29–0.90 d−1) and long-term (turnover rate < 0.04 d−1) stores supplied most N used in leaf growth. Compared to dominant (well-lit) plants, subordinate (shaded) plants relied more on mobilization from long-term N stores to support leaf growth. These differences correlated well with the C-to-N ratio of growth substrates and were associated with responses in N uptake. Based on this, we argue that internal regulation of N uptake acts as a main determinant of the importance of mobilized long-term stores as a source of N for leaf growth.
Most plants store carbon (C) and nitrogen (N) either by accumulation of assimilation/uptake overflows, or by deposition of specific reserve or recyclable compounds (Chapin et al., 1990). Stores are continuously built up or mobilized, hence interacting directly with the provision and utilization of plant resources. Allocation of C and N toward, and mobilization from, storage pools has been described in many species, and the effects of changes in growth conditions on such patterns are relatively well known (Millard, 1988; Chatterton et al., 1989; Chapin et al., 1990; Volenec et al., 1996). Conversely, the role of mobilized stores in supplying plant C and N growth demand is less clear, particularly regarding vegetative growth of perennial species.
One reason is simply lack of information. Studies of the role of stores require accurate identification of substrates derived from distinct sources, which poses methodological problems. Steady-state labeling techniques proved particularly useful in discriminating the use of newly acquired versus already available resources in response to specific stimuli, such as defoliation (de Visser et al., 1997). This does not, however, provide a quantitative partitioning of the sources supplying growth. The reason is tracers often move through a number of pools before being incorporated into growing tissue, which diminishes their usefulness to discriminate specific sources. Analysis of tracer time course can help infer the number and kinetics of mixing pools (Moorby and Jarman, 1975; Rocher and Prioul, 1987). This approach, frequently applied in studies of C and N fluxes in source systems, has received less attention in analyses of sources supplying sinks.
Another reason hindering the understanding of the role of stores is the absence of knowledge about its ecophysiological determinants. Often, the relative importance of stores as a source of substrates for growth tends to be associated with changes in mobilization intensity. However, it may depend as much on these as on responses of actual acquisition and demand rates (Bausenwein et al., 2001). Further, C and N metabolisms are closely interrelated: Mobilization of organic N implies mobilization of amino-C, C status affects N mobilization and uptake (Thornton et al., 2002), and N status affects C assimilation and storage and possibly mobilization, too (Stitt and Krapp, 1999). Such interdependent relationships, central to putative mechanisms controlling acquisition, storage, and mobilization of both C and N (Touraine et al., 1994; Stitt and Krapp, 1999; Farrar et al., 2000), need to be integrated in analyses of the role of stores in supplying growth.
The aims of this study were to (1) develop a method able to distinguish and quantify the sources of C and N supplying leaf growth in grasses, and then (2) use it to analyze the influence of plant status on the relative importance of those sources with the hypothesis that mobilization from stores is of greater importance for leaf growth when acquisition of C or N is more limited. In order to do this, a previously described method for estimating import of C and N substrates into leaf growth zones (Fig. 1; Lattanzi et al., 2004) was further developed to account for labeled and nonlabeled components, and coupled to steady-state labeling of C assimilation and N uptake. The time course of tracer incorporation into imported C and N substrates was then analyzed with compartmental models, with which the size and turnover of the substrate pool supplying leaf growth, and the contribution to it of current assimilation/uptake and mobilization from stores, were estimated.
The leaf growth zone of a grass tiller. Continuous production of cells at basal positions and their subsequent expansion gives rise to a flux of tissue and tissue-bound C and N out of the growth zone (EC and EN). This export is counterbalanced by import (deposition) of C and N substrates. The growth zone can thus be conceived as a place where substrates are imported, transformed, and then exported as structurally and functionally differentiated tissue. The velocity of an element moving through the growth zone (ν) increases until it equals the LER when cell elongation ceases. Lineal density of C and N (ρLC and ρLN) may increase after cell expansion ceases but becomes stable near the end of the cell maturation zone. Consequently, EC and EN can be estimated as LER times ρLC and ρLN of RPT. In turn, import rates can be estimated by adding to EC and EN the negative or positive variation in growth zone C and N mass.
The relative contribution of these sources was evaluated in plants of a C3 (Lolium perenne) and C4 (Paspalum dilatatum Poir.) grass growing undisturbed in mixed stands. Contrasting C3/C4 balances of the stands were brought about by moderately high (23°C) and low (15°C) temperature regimes. This exploited the differential growth response of C3 and C4 species to temperature to place L. perenne and P. dilatatum plants in contrasting hierarchical positions within the stands, and thus produce dominant and subordinate individuals with different rates of C assimilation and N uptake (Table I; see also Lattanzi et al., 2004).
Plant status at the beginning of labeling
L. perenne and P. dilatatum plants grew undisturbed in mixed stands for either 8 or 10 weeks at 23°C or 15°C, respectively, and thus in dominant and subordinate hierarchical positions. PPFDint indicates the proportion of PPFD intercepted by the C3 and C4 components within each stand. C assimilation and N uptake rates were measured over one light period (12 h). Means ± se (n = 4 to 12).
RESULTS
Import of C and N into the Growth Zone and Tracer Import in Briefly Labeled Plants
Sequential analysis of leaf elongation rate (LER) and of lineal density of C and N along the immature part of leaves showed that import of C and N into the leaf growth zone (〈IC〉 and 〈IN〉; see Table II for definition of symbols in text) was approximately constant along the experimental period. This was true for both species (L. perenne and P. dilatatum) and growth conditions (mixed stands at 23°C and 15°C). At several times during the experimental period, C assimilation and N uptake were labeled over the photoperiod immediately preceding sampling (briefly labeled plants). This demonstrated that the contributions of recent C assimilation and N uptake were also constant (Fig. 2). Such constancy suggests a system in steady state with respect to fluxes of C and N and the contributions of recently assimilated C and absorbed N.
Definitions of symbols used in text
Import of total (•, ▴) and briefly labeled (▵, ○) C (〈IC〉; •, ○) and N (〈IN〉; ▴, ▵) substrates into the leaf growth zone. Growth conditions as for Table I. Lines indicate average values. Error bars are 1 se of import values.
Besides their stability, a remarkable feature of the results was the contrasting behavior of C and N substrates. Between 48% and 64% of the C imported over a day (i.e. 24 h) derived from C assimilated during the previous 12-h light period. Conversely, N taken up over the same period contributed only 3% to 17% to total daily N import.
Modeling of Tracer Time Course in Continuously Labeled Plants
Continuous steady-state labeling was applied to follow the saturation kinetics of label in the different C and N pools that served as sources for leaf growth. Continuous labeling thus meant that C and N tracers entered the plants over the whole 15-d (23°C) or 18-d (15°C) experimental period. In this case, the proportion of tracer in imported C and N substrates (
and 
) was not stable, as in briefly labeled plants, but characterized by rapid increases followed by either slower increases or complete stabilization (Fig. 3). In six out of the eight situations, a two-pool model adequately described this biphasic pattern. The model consisted of a substrate pool (Q1) supplying the growth zone, which was in turn supplied by newly acquired C or N, and by mobilization from a storage pool (Q2). Mass transfers were governed by first-order rate constants (k10, k12, and k21; numbers refer to donor and receiver pools, respectively). In two cases (
in P. dilatatum subordinate and 
in L. perenne dominant plants), the data fitted well to a one-pool model, a reduced case of the former where k21 becomes infinitely small (Fig. 4). Optimized and derived model parameters are given in Table III.
The proportion of labeled C (•, 
) and N (○, 
) in substrates imported into the leaf growth zone of continuously labeled plants estimated with Equation 3b. Growth conditions as for Table I. Lines show model predictions (solid lines) ±se of predicted values (dotted lines). Error bars indicate ±se of estimated values.
Two-pool (a) and one-pool (b) models of sources supplying leaf growth. Newly acquired (i.e. labeled) C or N (X) first enter Q1 (TX). From there, they are either imported into the growth zone (IX) or exchanged with Q2 (DX and MX). In solving the model, steady-state and first-order kinetics were assumed, that is, pool sizes are constant in time and fluxes are the product of pool size times a rate constant (k10, k12, and k21; numbers referring to donor and receptor pools, respectively). In the one-pool model, ϕ is the fraction of nonlabeled C or N entering the system, MX/(MX + TX), i.e. the proportional contribution of MX in supplying Q1. In the two-pool model, ϕ corresponds to k12/(k10 + k12), the average probability of an atom being exchanged through Q2 before its import into the growth zone.
Model optimization results
Total C or N import into the growth zone (〈IX〉) and first-order rate constants (k10, k12, and k21; numbers refer to donor and receptor pools, respectively) fitted to two- or one-pool models describing the time course of C and N tracer incorporation into substrates supplying leaf growth (Fig. 4). Models were run with a 0.1-d time step, and predicted values integrated to give 1-d averages. Estimated parameters and 〈IC〉 and 〈IN〉 were used to compute pool size (Q1 and Q2), turnover rate (the ratio of atoms passing through relative to the total amount present in each pool), and half-time (t0.5; the average time an atom will reside in a pool = −ln (0.5)/turnover rate), and the contribution of MX to IX [ϕ = MX/(MX+TX)]. In the two-pool model, the turnover rate of Q1 is k10 + k12, while turnover rate of Q2 is simply k21. In the one-pool model, the turnover rate of the only pool is k10. Means ± se (n = 6). For comparison, Q1 and Q2 are shown as a proportion of tiller mass and of C assimilation and N uptake rates. Growth conditions as for Table I. n.e., Not estimated.
Models described well the time course of 
and 
as indicated by the close agreement of values estimated by Equation 3b and model predictions (Fig. 5). The se of predicted values (Fig. 3), as well as of derived parameters (Table III), was within reasonable limits, with coefficients of variation below 20% for Q1 and close to 35% for Q2C. However, k21N had comparatively large se, which clearly reflected on the se of its derived parameter, Q2N (Table III).
Comparison between estimated (Eq. 3b) and predicted (model) values of the fraction of labeled C (
; •, ▪, ▴, ▾) and N (
; ○, □, ▵, ▿) into the growth zone for continuously labeled L. perenne (•, ○, ▪, □) and P. dilatatum plants (▴, ▵, ▾, ▿) at 23°C (•, ○, ▴, ▵) or 15°C (▪, □, ▾, ▿). Growth conditions as for Table I. The line indicates the y = x relationship.
Model Validation
Formally analogous models able to identify Q1 and Q2 with biochemically and spatially defined metabolites have been evaluated by comparing predicted and estimated values (e.g. Suc in leaves; Rocher and Prioul, 1987). In this study, the system was too complex to expect such direct correspondences, for there are several intermediaries with unknown spatial compartmentalization between CO2 assimilation/NO3− uptake and subsequent import of C and N into the leaf growth zone, mainly as Suc and amino acids (Schnyder and Nelson, 1988; Gastal and Nelson, 1994). As an alternative, sizes of Q1 and Q2 were compared to tiller average C and N mass. This revealed Q1C represented between 1.1% and 1.8% of tiller C mass. Since IC was underestimated due to unaccounted respiration, Q1C values should have been approximately 30% higher (Volenec and Nelson, 1984). Conversely, Q1N represented 10% to 30% of tiller N mass (Table III).
Smaller relative size of Q1C than Q1N was a common feature in all plants. But the magnitude of this difference varied greatly. Dominant plants had relatively larger Q1C than subordinate plants. The opposite was true for Q1N. As a result, the ratio of Q1C to Q1N (Q1C:N) discriminated well subordinate from dominant plants (1.0 versus 2.9 in L. perenne; 0.5 versus 1.9 in P. dilatatum). Strictly comparable data are scarce. Farrar (1990) estimated a pool supplying C for shoot growth in three C3 grasses as 1.4% to 5.8% of plant C mass, whereas Yoneyama et al. (1987) computed a pool supplying protein synthesis in Brassica campestris leaves equal to 14% of total plant N.
Q2 values were reasonable for C, although somewhat high in subordinate L. perenne plants. But Q2N values were 2 to 3 times greater than the average N mass of tillers (Table III). Clearly, despite the models' good statistical behavior, Q2N values were unrealistic. Knowledge of N cycling within grasses indicates that a delay might occur between tracer incorporation (e.g. into growing tissue) and its subsequent mobilization (e.g. from senescing tissue; Robson and Deacon, 1978; Millard, 1988; Thomas, 1990). To test whether such a divergence from assumed homogeneous well-mixed pools would affect estimated pool size, a lag time between tracer incorporation in Q2 and subsequent mobilization was included in the model. Optimization yielded lag times close to 14 d for plants at 23°C, and of 16.7 d for plants at 15°C. These values were close to leaf expansion durations (Table I), which agrees with net N mobilization starting near the time a leaf is fully expanded (Robson and Deacon, 1978). Consideration of a delay strongly reduced the size of Q2N to 74% to 87% of tiller N mass (compare with Table III). Importantly, the lag time had only minor effects on size of Q1 (<3%) and on the contribution of mobilization from Q2N (ϕ; <4%).
Taken together, these results indicate Q1 size and half-time, and ϕ, i.e. parameters derived from k10 and k12, were adequately estimated. On the other hand, Q2 size and half-time were less accurately estimated, particularly for N, and should therefore be interpreted with more caution. The existence of a lag time between tracer deposition and mobilization in Q2 seems worthy of further study, but data at longer timescales are necessary for stable model solutions.
The Age of Imported Substrates and the Sources Supplying Them
Models were used to determine the time elapsed between C and N tracers entering the plant and their arrival at the growth zone. This yielded an age profile of imported substrates, where import of nonlabeled C or N at the end of the experimental period was described as older than 15 (23°C) or 18 (15°C) d. Additionally, in the two-pool model, the total amount of C and N tracer derived from each day's assimilation/uptake was further separated into the fraction derived from mobilization from Q2 and that only cycling through Q1 (Fig. 6).
Age of substrates imported into the leaf growth zone. Import of C and N (〈IC〉 and 〈IN〉) is shown as a function of the number of days elapsed between assimilation/uptake and subsequent deposition in the growth zone. For each day, the white part of the bar indicates substrates that cycled only through Q1, while the dark part refers to substrates exchanged with Q2 prior to import (see Fig. 4). Growth conditions as for Table I.
In all treatments, imported C derived mostly from very recent photosynthesis. A high proportion, 50% to 65%, corresponded to the C reaching the growth zone within 12 h of entering the plant (day 1). The remaining C was chiefly imported during the following dark and light periods (day 2). Virtually all this C cycled only through Q1C. Thus, Q1C effectively acted as a short-term store buffering day/night cycles.
Opposite to C results, the importance of very recent N uptake was small: N derived from same-day uptake (day 1) represented less than one-sixth of the total imported N. Imported N derived in more or less similar proportions from last 3-d (23°C) to 5-d (15°C) uptake. Again, most of this recent N cycled only through Q1 (compare with Fig. 6). With half-times >0.8 d, stores within Q1N would have been involved in moderating day-to-day variations in supply.
Mobilization from Q2 supplied <10% of imported C. Q2C half-times, approximately 10 d, were close to leaf expansion duration (Table I). The exceptions were subordinate L. perenne plants, where Q2 provided 27% of imported C and had a half-time twice as long and more similar to that of Q2N (Table III). Conversely, the contribution of mobilized N was variable but relevant in all plants. In subordinate plants, N mobilized from Q2 was the preponderant source, supplying three-fourths of imported N. In dominant plants, it was more important in the C4 (41%) than in the C3 (24%) grass. Half-times of Q2N were relatively long, but these must be interpreted with caution because they implied unrealistically large pool sizes. Consideration of a lag time, which could have caused such behavior, reduced half-times to 8 (subordinate L. perenne), 10 (subordinate P. dilatatum), and 19 (dominant P. dilatatum) d (compare with Table III). When added to the 14- to 16-d lag times, these values become closer to leaf lifespans (Table I).
DISCUSSION
On the Approach
This article presents a novel approach for analyzing the contribution of distinct sources in supplying C and N substrates for growth. It involves three steps: (1) estimating C and N import into growth zones; (2) determining their labeled fraction under steady-state labeling of C assimilation and N uptake; and (3) analyzing the time course of these fractions with compartmental models. The first is an extension of a previously presented method, based on well-established knowledge of C and N fluxes within growth zones (Lattanzi et al., 2004). Steady-state 13CO2/12CO2 and 15N/14N labeling (de Visser et al., 1997; Schnyder et al., 2003), as well as modeling of tracer time courses (Moorby and Jarman, 1975; Prosser and Farrar, 1981), are established techniques, too. New is their concerted use to quantitatively resolve the sources supplying growth.
Compared to prior labeling studies, this approach imports several advancements. First, deposition of C and N in the growth zone is solely driven by the demand of dividing, expanding, and maturing cells (Schnyder and Nelson, 1988; Allard and Nelson, 1991; Gastal and Nelson, 1994). Hence, confounding effects of processes others than these, which also affect tracer content of leaves but bear no direct association with their growth (e.g. carbohydrate and amino acid storage in either growing, mature, or senescent leaves), are obviated.
The second advantage derives from explicitly addressing tracer mixing. In doing so, the assumptions customary to compartmental analysis are made. Some of these are a major restriction in applying this approach, such as that of a system in steady state; some are of difficult validation, such as that of homogeneous, well-mixed pools. Indeed, aggregating C and N metabolism into one or two pools is a simplification that is almost certainly invalid. Still, we think the approach renders a more meaningful interpretation of tracer fluxes than assuming close correspondences between labeled and nonlabeled substrates and the sources supplying them. For instance, in six out of eight modeled situations, the fraction of imported tracer deriving from current assimilation/uptake varied from virtually 1.0, for tracer imported within 1 d of its assimilation/uptake, to <0.5, for tracer imported 3 d (C) or 5 to 10 d (N) after its assimilation/uptake (Fig. 6). In these cases, assuming invariant relationships would have been misleading, revealing the importance of analyzing time courses.
Finally, use of a modeling approach allowed the realization that the substrate pool (Q1) acted as a short-term store and that the ratio of C-to-N substrates indicated plant C/N status (see below). Such insights would have otherwise been difficult to gain.
Nature and Importance of the Growth Substrate Pool, Q1
As the pool directly supplying the growth zone, Q1 is analogous to a growth substrate, i.e. compounds readily available for tissue synthesis. A distinct feature of Q1 was its contrasting kinetics for C and N substrates. In all cases, Q1C had a shorter half-time than Q1N (Table III).
At first glance, such a difference might be thought to be related to a greater proximity, either physical or metabolic, of CO2 fixation to subsequent import of Suc into the growth zone than that of NO3− uptake from amino acid import. Short-term labeling studies do show that photoassimilates can reach the leaf growth zone within 10 to 20 min, whereas it would take at least 1 h for newly absorbed N to do the same (compare with Cooper and Clarkson, 1989; Allard and Nelson, 1991; Thorpe and Minchin, 1991; Hayashi et al., 1997). But this cannot be a significant determinant of observed 0.5- to 2-d shorter half-times of Q1C than Q1N. The nature of such differences must therefore be related to kinetics of storage components included within Q1. Indeed, analysis of Figure 6 revealed that Q1 acted not only as a gateway for substrates in transit to the growth zone, but also as a short-term store (see “Results”). Q1C represented <2% of tiller C mass and was small compared to C gain, while Q1N constituted 10% to 30% of tiller N mass and represented several days of N uptake (Table III), further supporting the idea of a greater storage component within Q1N than Q1C.
We are not aware of any other comparative study of C and N growth substrate kinetics with which these results could be compared. Compartmental analyses of source photosynthetic leaves have consistently indicated a cytoplasmic/apoplastic (Suc) transport pool, and a vacuolar (Suc)/chloroplastic (starch) storage pool with half-times <2 h and 12 to 25 h, respectively (Rocher and Prioul, 1987; Farrar, 1990, and refs. therein). Conversely, source metabolism of N is more tortuous, as nitrate and amino acids are subject to storage in both roots and shoot. For root nitrate, half-times <15 min and 4 to 7 h are typical for transport and vacuolar pools (Devienne et al., 1994, and refs. therein). Amino acids often account for a substantial part of soluble N, but there are hardly any data on their kinetics. A study in B. campestris suggests half-times <35 min, and 3 to 20 h for transport and storage pools (Yoneyama et al., 1987). Further, xylem/phloem-cycling amino acids, a general feature in C3 and C4 species (Touraine et al., 1994; Engels and Kirkby, 2001), could act as a labile reserve (Cooper and Clarkson, 1989).
Contrasting these values with Q1 half-times (Table III) leads to three conclusions. First, it corroborates that Q1C and Q1N included storage components; neither Q1C nor Q1N labeling kinetics were as fast as expected if accounted for solely by transport pools. Second, differences of 0.5 to 2 d between Q1C and Q1N half-times can be explained only by differences in the kinetics of C and N storage (not transport) pools. Thus, Q1C half-times <0.5 d resemble a flux-weighted average of transport and storage pools in source leaves. But Q1N half-times ≥0.9 d suggest N tracer entered storage pools more than once before reaching the growth zone. Third, the more straightforward metabolism of C, compared to that of N, makes a more important storage component within N than C growth substrates highly probable, independent of species or growth conditions. Inherently different kinetics of C and N growth substrates may be a consequence of contrasting buffering requirements. Whereas C acquisition has an abrupt diurnal cycle, N uptake is likely to experience more drastic changes at timescales of days due to the pulsed and patchy nature of external N availability (Chapin et al., 1990). Plants deprived from N are able to maintain unaltered growth rates for several days. By using supply interruption cycles, Macduff and Bakken (2003) estimated this buffer capacity to be between 0.5 to 3 d, which compares favorably with Q1N half-times (Table III).
The Role of Mobilized Stores in Supplying Leaf Growth
Stores were an integral part of the supply of C and N substrates for leaf growth in all plants, providing about one-half of C and >80% of N imported into the leaf growth zone (Fig. 6). We hypothesized the importance of mobilized stores in supplying leaf growth increases whenever resource acquisition becomes limited. This was not the case for C. Subordinate plants had low C assimilation rates, closely related to their reduced light capture (Table I). But C older than 2 to 3 d contributed little to C import, corroborating the notion that leaf growth relies on recent assimilates (Anderson and Dale, 1983, but see below), and stores within Q1C were important in supplying leaf growth in both dominant (well-lit) and subordinate (shaded) plants (Fig. 6). This substantiates results from experiments with Arabidopsis (Arabidopsis thaliana) starchless mutants, which showed that the relationship between daily starch turnover and plant growth is not affected by light intensity (Schulze et al., 1991). Although the actual contribution of C stores to growth was not assessed in that study, its consideration together with present results strongly suggests that the importance of short-term C stores in supplying growth is closely associated with the need of buffering day/night cycles and independent of C assimilation rates. Similar responses of P. dilatatum and L. perenne suggest analogous mechanisms in starch-storing (C4) and Suc/fructan-storing (C3) grasses.
In exception to this general pattern, mobilization from Q2C provided 27% of imported C in subordinate L. perenne plants (Table III). In vegetative grasses, there are two potential sources of old C: carbohydrate stores in sheath bases and stems (Chatterton et al., 1989; Thom et al., 1989) and amino-C derived from protein turnover. Compared to dominant L. perenne plants, the higher contribution of old C observed in subordinate plants seems strictly associated with differences in mobilized amino-C (Table IV). This is also suggested by a longer half-time of Q2C in subordinate L. perenne, close to that of Q2N and to the leaf lifespan. Hence, when long-term N stores contribute greatly to N import, old amino-C could become an important source of C for leaf growth in C3 grasses. Interestingly, Thornton et al. (2004) found an important contribution of old C to root exudates, which includes a substantial amount of amino-C. Better understanding of C supply to growth processes clearly requires more information on amino-C fluxes.
Estimated contribution of carbohydrates (CHO-C) and amino-C to imported C
Amino-C import was estimated as N import times an assumed C:N ratio of 2.6 (w/w) for imported N compounds (see Schnyder and de Visser, 1999; Amiard et al., 2004). Estimation of Q2-derived amino-C further assumed that there was a strict association between unlabeled C and unlabeled N within organic N compounds. Growth conditions as for Table I. n.e., Not estimated.
Notably, this flux of old C was absent in subordinate P. dilatatum plants, even though long-term N stores supplied an equally significant part of imported N. In fact, calculations in the C4 grass indicated more Q2-derived amino-C than the total amount of Q2-derived C, and thus an unrealistic, higher than 100%, contribution of amino-C (Table IV). The reason for this difference between L. perenne and P. dilatatum is not clear. Varying the assumed C-to-N ratio affected estimated values only slightly. Hence, the fault probably resides in assuming a strict relationship between unlabeled C and unlabeled N within organic N compounds.
Stores provided most of imported N in all treatments. However, this similarity hid contrasting responses. In dominant plants, stores within Q2N were important, particularly in L. perenne (Fig. 6; Table III). The comparatively low importance of mobilization from Q2N plus the short half-time of Q1N determine that, in fact, N import in these plants depended strongly on continuous uptake. Conversely, leaf growth in subordinate plants of both species became largely independent of external N, as 75% of imported N derived from Q2N. However, there is no indication that dominant and subordinate plants would have used qualitatively different N sources. In all plants, the lag time between incorporation and mobilization of N tracer in Q2N, and the close relation between the half-time of this pool and leaf lifespan, suggest that mobilization of long-term N stores was associated with leaf turnover. This agrees with long-term N stores in grasses being largely accounted for by export of amino acids from senescing leaves (Millard, 1988; Feller and Fischer, 1994; Bausenwein et al., 2001).
Interestingly, Q1C:N was closely and negatively correlated with the fractional contribution of Q2N to IN, accommodating differences between plants growing in contrasting hierarchical positions, and also between L. perenne and P. dilatatum dominant plants (Fig. 7). This indicates that the importance of long-term stores in supplying N for leaf growth was related to the relative abundance/scarcity of C and N substrates. There is now convincing evidence for nitrate uptake being controlled by plant N demand through the regulatory activity of C and N metabolites, indicators of plant C/N status (Touraine et al., 1994; Stitt and Krapp, 1999). A possible explanation for Figure 7 is that such a mechanism is also a determinant of the importance of long-term N stores for leaf growth. Support for this possibility is lent by the fact that the partitioning of N uptake between dominant and subordinate plants within each stand was fairly similar to that of intercepted photosynthetic photon flux density (PPFD), and hence of C assimilation (compare with Table I), which suggests that uptake rates of subordinate plants were limited by their lower C/higher N plant status (Gastal and Saugier, 1989). Crucially, these differences were not caused by variations in N external availability because all plants had access to full nutrient supply.
Correlation between the ratio of C-to-N growth substrates (Q1C:N) and the relative contribution of long-term stores (Q2N) to N import into the leaf growth zone, ϕ[MX/(TX + MX)]. Data are from L. perenne (•, ▪) and P. dilatatum (▴, ▾) plants growing in mixed stands at 23°C or 15°C, and thus in dominant (•, ▴) or subordinate (▪, ▾) positions. Error bars indicate ±se. *, P < 0.01.
In suggesting a causal link between the importance of long-term stores and the internal capacity for N acquisition, this explanation is consistent with our working hypothesis. A corollary of plant C/N status controlling both N acquisition and the importance of long-term stores is that, whenever possible, a plant will acquire and use new N rather than stores (and increase its growth rate). This might not be so in all grasses. Farrar (1990) suggested the inherent difference between slow- and fast-growing species is their use of C stores. A differential use of nutrient stores seems a plausible possibility worthy of experimental testing.
CONCLUSIONS
A novel approach to study the sources of C and N supplying leaf growth is presented. Compared to previous analyses, it imports a more mechanistic definition of the process studied, an increased accuracy with which it is measured, and a more meaningful interpretation of tracer fluxes. By using such an approach, we were able to show that stores were a critical part of the supply of C and N substrates for leaf growth in both L. perenne (C3) and P. dilatatum (C4). Long-term carbohydrate stores were of little relevance for leaf growth in these undisturbed plants, and short-term C stores had an important role in buffering light/dark cycles in all plants. Hence, no evidence was found of a causal link between C acquisition and the importance of C stores in supplying leaf growth. Long-term N stores were important in supplying leaf growth in all situations, but particularly in plants where growth was more C than N limited and N uptake capacity was lower. It is proposed that a common mechanism regulates N acquisition and use of N stores.
MATERIALS AND METHODS
In the following, we will first describe the labeling facility employed, along with the plant material and growth conditions used to generate stands with contrasting C3/C4 balances. Next, we will detail the two labeling strategies (and associated sampling schemes) used, and formulas to calculate import of total and labeled C and N into the growth zone. Last, we will present the models used to describe tracer kinetics, the assumptions made in solving them, and their (partial) verification.
13CO2/12CO2 Labeling Facility
The equipment and principles involved have been detailed elsewhere (Schnyder et al., 2003). In brief, four growth cabinets (E15; Conviron, Winnipeg, Manitoba, Canada) were operated as open gas exchange systems. Air supply was generated by mixing CO2-free air with CO2 of known δ (the deviation of the 13C-to-12C ratio in CO2 from that of the international standard, Vienna Pee Dee Belemnite [VPDB]). Periodic adjustments of airflow and CO2 concentration in the inlet maintained a constant CO2 concentration of 300 μL L−1 within the growth cabinet. Two growth cabinets received 13C-enriched CO2 (Messer Griesheim, Frankfurt; δ − 2.9‰, cabinets I and II), and two 13C-depleted CO2 (Messer Griesheim; δ − 47.0‰, cabinets III and IV).
Plant Material and Growth Conditions
Plant material and growth conditions have also been detailed previously (Lattanzi et al., 2004). Briefly, mixed Lolium perenne/Paspalum dilatatum stands were constructed by arranging 178 pots (i.d. 50 mm, length 350 mm) with one seedling of either L. perenne or P. dilatatum into plastic boxes (0.43 m2). Two such stands were allocated to each of the four growth cabinets.
Plants grew under a 12-h photoperiod, with a PPFD of 550 μmol photons m−2 s−1 at canopy height, provided by cool-white fluorescent lamps. Vapor pressure deficit was controlled at 0.5/0.3 kPa (light/dark) in all growth cabinets. An automated irrigation system watered the stands four times a day, flooding the boxes for 30 min with modified one-half-strength Hoagland solution (105 mg N L−1 supplied only as nitrate). Stands were periodically flushed with distilled water to prevent salt accumulation.
Canopy air temperature was set at 25°C/23°C (light/dark) in two growth cabinets (cabinets II and III), and at 15°C/14°C (light/dark) in the other two cabinets (cabinets I and IV). These temperature regimes resulted in daily average sand temperatures (15 mm below surface) of 23°C and 15°C, respectively.
After 8 weeks of growth at 23°C, P. dilatatum individuals were larger and taller than their L. perenne neighbors. The opposite occurred in mixed stands at 15°C (Table I). Within each mixture, larger individuals will be referred to as dominant plants and the others as subordinate plants.
Partitioning of Intercepted PPFD
On day 0, after either eight (23°C) or 10 (15°C) weeks of uninterrupted growth, PPFD and plant leaf areas were recorded at 15-cm increments from top to the bottom of the stands. Intercepted PPFD was then partitioned between the C3 and C4 grass by weighting the fraction of PPFD intercepted at each canopy stratum by the proportional contribution of each species to the leaf area present in that stratum.
Leaf Turnover
Leaf expansion duration and leaf lifespan were estimated from biweekly measurements of leaf appearance rate, number of growing leaves, and number of green leaves in a set of 12 tillers per treatment (Davies, 1993).
Labeling Strategies and Sampling Times
Plants were sampled in a series of six harvests at day 0, 1, 2, 4, 8, and 15 (23°C), or at day 0, 1, 2, 5, 12, and 18 (15°C). There were two labeling strategies associated with these harvests: Assimilated C and absorbed N were labeled either briefly (last 12 h, i.e. the entire photoperiod prior to sampling) or continuously (since day 0) before sampling (Fig. 8). In both strategies, C labeling was performed by swapping individual plants between growth cabinets, which resulted in the exposure of plants grown under 13C-enriched CO2 (δ − 2.9‰) to 13C-depleted CO2 (δ − 47.0‰), and vice versa. In the case of N, the 15N enrichment of nutrient solution watering cabinets I and II was increased from 0.37 atom% (natural abundance) to 1.1 atom% immediately after the first harvest (Fig. 8).
The relationship between labeling and sampling times for briefly (A) and continuously (B) labeled plants. Arrows indicate times of plant transfers, dotted vertical lines indicate sampling times (harvests 1–6), and gray shades show the resulting periods over which assimilated C and absorbed N were labeled. All harvests were done immediately after lights went off. Black and white bars in the time axis indicate dark and light periods, and the white asterisk indicates the time when the 15N enrichment of the nutrient solution watering cabinets I and II was changed.
In the case of briefly labeled plants, four plants per species and temperature regime were swapped during the dark period preceding the labeling photoperiod. Previously, plants were flushed with 0.5 L of distilled water. Importantly, labeling commenced only once lights went on. For C, the reason is obvious. For N, this was so because the first irrigation event after the plant transfer was scheduled immediately before the start of the light period.
In the case of continuously labeled plants, 16 plants per species and temperature regime, different from briefly labeled ones, were swapped during the dark period after the first harvest: eight plants per species transferred from cabinet II to cabinet III, plus eight from cabinet III to cabinet II (23°C treatment), and the same for cabinets I and IV (15°C treatment; Fig. 8). Previously, stands were flooded with distilled water three successive times.
Sample Collection and Analysis
At each harvest, 10 plants per treatment (five per growth cabinet) were sampled from the central part of the stands: Four were continuously labeled plants, two were briefly labeled plants, and four were nonlabeled (control) plants. In each plant, the growth zone and an immediately adjacent piece of recently produced tissue (RPT) were dissected out of two to three growing leaves whose length was approximately one-half that of the youngest fully expanded leaf present in the tiller (Fig. 1; for details, see Lattanzi et al., 2004). The rest of the plant, including root and shoot, was pooled into one sample. After dissection, samples were heated to 100°C for 1 h to inactivate metabolism irreversibly, then dried during 48 h at 65°C. This procedure might alter some labile metabolites, but it causes no measurable loss of total C or N (Cone et al., 1996), which is the subject of this work. Once dry, samples were weighed and then milled. C and N content and 13C:12C and 15N:14N isotope ratios were determined on 1-mg dry-matter aliquots using a CHN elemental analyzer (NA1500; Carlo Erba Strumentazione, Milan) interfaced to a continuous-flow isotope mass ratio spectrometer (Delta plus; Finnigan MAT, Bremen, Germany). From isotope ratios, molar fractions were calculated (atom%, A). Standards were run every 10 samples (sd = 0.25‰ for δ; sd = 0.0017% for 15N atom%).
Calculation of the Proportion of Tracer in a Sample
The proportion of C or N (X) tracer in a sample is directly proportional to the content of 13C or 15N in it. Therefore, the amount of 13C or 15N in each sample (Aspl X) was expressed as a lineal function of the fractions of labeled and unlabeled C or N (flab X and funlab X), and the 13C or 15N content of analogous samples from control plants (Alab X and Aunlab X),
(1a)
For example, for a plant swapped from cabinet II to cabinet III, Aunlab C and Alab C correspond to the amount of 13C of samples taken from control plants continuously grown under CO2 with δ − 2.9‰ and CO2 with δ − 47.0‰, respectively. Aunlab C and Alab C were determined, for each harvest date, in two plants per species and temperature regime. Aunlab N was determined, for each harvest date, in four plants per species and temperature regime sampled from cabinets III and IV, i.e. irrigated with nonenriched nutrient solution. Alab N was not experimentally determined but assumed to be equal to 1.1%, i.e. the 15N enrichment of the labeling solution. This implies that eventual discrimination effects were ignored. Given the enrichment used, the error introduced in the measurement of flab N was small: <1.0% for a 20‰ discrimination.
Since funlab X = 1 − flab X, Equation 1a can be solved for flab X:
(1b)
The amount of labeled C or N was then calculated as the C or N mass of the sample times the corresponding flab C or flab N value.
Estimation of C Assimilation and N Uptake Rates
C assimilation and N uptake were estimated as the total amount of C and N tracer found in whole briefly labeled plants. For C, this is a measure related to daytime C gain, although respiration of nonlabeled C is unaccounted for. In the case of N, this measure is close to a net balance between N influx and efflux over the 12-h light period.
Estimation of Import of Labeled and Nonlabeled C and N into the Leaf Growth Zone
Briefly Labeled Plants
The amount of labeled C or N imported into the growth zone was estimated as the sum of labeled C or N present in the growth zone plus labeled C or N present in the piece of RPT (Fig. 1). Clearly, this assumes all imported tracer would still be present in these leaf segments at the end of the 12-h labeling period. This was likely true. On one side, deposition of C and N outside the growth zone is very small relative to deposition within the growth zone (Allard and Nelson, 1991; Gastal and Nelson, 1994; Maurice et al., 1997). On the other, the sampled piece of RPT corresponded to tissue exported during the last 16 to 19 h (Lattanzi et al., 2004). Therefore, all imported tracer would have been initially deposited within the growth zone, and any tracer exported out of the growth zone by tissue-bound efflux during the 12-h labeling period would still be present in the piece of RPT.
Continuously Labeled Plants
The simple estimation of import of labeled C and N in briefly labeled plants could not be extended to continuously labeled plants. This is because, after 16 to 19 h, part of the imported tracer would have left the “growth zone + RPT” segment due to tissue-bound efflux (Fig. 1). A new approach was therefore taken based on a previously presented method to estimate fluxes of C and N through leaf growth zones (Lattanzi et al., 2004).
Tissue-bound C and N export out of the growth zone (EX) was estimated over 1-d intervals (ti−1 − ti) as the product of leaf elongation rate (〈LER〉, where 〈 〉 denotes 24-h averages) and the density of C and N per unit length in RPT (〈ρLX〉). Import of substrates into the growth zone (〈IX〉) was then estimated by adding the negative or positive variation in mass of the growth zone over that time interval 
Respiration was ignored, thus underestimating actual C import by the amount lost through respiration. This measure of import is analogous to a net deposition rate (Allard and Nelson, 1991; Maurice et al., 1997; Schäufele and Schnyder, 2001). Essential prerequisites for the validity of these calculations are the specific definition of the growth stage of sampled leaves and the precise location and size of the piece of RPT where 〈ρLX〉 is determined (for details and validation, see Lattanzi et al., 2004).
This method was further developed to estimate the labeled and nonlabeled components of C and N fluxes. The amount of labeled C or N imported into the growth zone (Ilab X) was assessed for 1-d intervals as the export of labeled C or N (Elab X) plus the variation in mass of labeled C or N within the growth zone (Glab X) over the same time interval:
(2)
The fraction of labeled C or N in import 
thus results:
(3a)
Since IC and IN, as well as GC and GN, were constant in time (at a 1-d timescale; Lattanzi et al., 2004), then EX = IX, and 
Hence, 
can be directly estimated as:
(3b)
Equation 3b explicitly shows that estimation of 
over a 1-d time interval (ti−1 − ti) required estimation of (1) 
over the same time interval; (2) 
at the lower (ti−1) and upper (ti) ends of the time interval; and (3) GX/〈IX〉. The former was estimated as the definite integral of nonlinear functions fitted to the time course of the fraction of labeled X in samples of RPT (
). Similarly, nonlinear functions fitted to the time course of the 
were used to estimated values at ti−1 and ti. Values of GX/〈IX〉 were taken from Lattanzi et al. (2004).
The five specific 1-d intervals over which 
was estimated were defined by the six harvest dates as follows: days 0 to 1, 1 to 2, 3 to 4, 7 to 8, and 14 to 15, and days 0 to 1, 1 to 2, 4 to 5, 11 to 12, and 17 to 18 for the 23°C and 15°C temperature regimes, respectively. To test the accuracy of the method, 
and 
values estimated through Equation 3b were compared with those directly measured in sets of briefly labeled plants. The regression of estimated versus observed values did not differ from the y = x line (P > 0.10; Fig. 9).
Comparison between values of the fraction of labeled C or N imported into the growth zone (
) measured in briefly labeled plants and estimated using Equation 3b. The line indicates the y = x relationship.
Modeling of Time Course of C and N Tracer Imported into the Growth Zone
We propose a two-pool model to describe the time course of the incorporation of tracer into the growth zone, which is formally similar to that used in compartmental analyses of C export from source leaves (e.g. Moorby and Jarman, 1975; Rocher and Prioul, 1987). Newly acquired (i.e. labeled) C and N first enter Q1. From there, C and N can be either imported into the growth zone or exchanged with Q2 (Fig. 4a). Assuming first-order kinetics—that is, fluxes are the product of pool size times a rate constant (k10, k12, and k21; numbers referring to donor and receptor pools)—the rate of change of each compartment with respect to time (t) is given by:
(4a)
(4b)
Assuming the system is in steady state, that is, dQ1/dt = dQ2/dt = 0, pool sizes are given by
(5a)
(5b)and fluxes by
(6a)
(6b)where I is the measured import rate, and k10, k12, and k21 are fitted parameters.
In some cases, a simpler one-pool model was proposed in which labeled and nonlabeled C and N enter Q1 and from there are imported into the growth zone (Fig. 4b). Hence, the one-pool model is the special case of the former, where Q2 becomes infinitely large and k21 infinitely small.
Assuming first-order kinetics,
(7)Then, under the steady-state assumption,
(8)
(9a)
(9b)where I is the measured import rate, and k10 and ϕ are fitted parameters. Note that ϕ equals k12/(k10 + k12) in the two-pool model.
Models were implemented in MODELMAKER (version 4.0; Cherwell Scientific, Oxford, UK). Differential equations were solved using the fourth-order Runge-Kutta numerical method, with a step size of 0.01 d. Predicted 
and 
were integrated over 1-d intervals, thus producing data comparable to measured values (i.e. 
and 
). A built-in optimization function was used to estimate the values of fitted parameters by iterative minimization of the sum of squared differences between measured and predicted 
and 
Verification of Assumptions
The models hold the assumptions customary of compartmental analyses. Two are explicit: (1) The system is steady and (2) fluxes obey first-order kinetics. Two are implicit: (3) Pools are homogeneous and well mixed and (4) 13C/12C and 15N/14N isotopic discrimination in exchanges can be neglected.
Regarding Assumption 1, previously presented data from this same experiment (Lattanzi et al., 2004) and from other studies (Allard and Nelson, 1991; Gastal and Nelson, 1994) show that growth of the most rapidly elongating leaf had indeed manifested steady-state dynamics when considered at 1-d intervals. Leaf elongation proceeded at a steady rate, and variations in ρLC and ρLN and in GC and GN were minor, hence EC and EN and IC and IN were virtually constant (Lattanzi et al., 2004). Further, import of C and N assimilated/absorbed over the 12-h light period, i.e. import of labeled C and N in briefly labeled plants, was approximately constant. Therefore, not only total C and N import but also their source composition appeared stable (Fig. 2). Steady state is here defined at a 1-d timescale, and diurnal cycles (Yoneyama et al., 1987; Schnyder and Nelson, 1988) were not considered by the model.
Eventual effects of noncontinuous incorporation of tracer, arising from diurnal cycles in C assimilation/N uptake, were assessed assuming that import of C and N into the growth zone (IX) was constant over the day, but tracer import (TX) occurred only over the 12-h light periods. Optimized solutions indicated consistent reductions of about 25% in size and half-time of Q1C in all modeled situations. However, these were unstable, probably due to a limited data set. Including a similar diel cycle in N uptake had no effect upon Q1N parameters. Since general responses were unchanged and because of a lack of quantitative support for eventual day/night changes in C and N import rates, we chose to retain the previous optimized values, observing that Q1C size and half-time would be overestimated.
Assumption 2 is, in a strict sense, probably false. However, support for its practical validity has been found repeatedly (Prosser and Farrar, 1981; Yoneyama et al., 1987; for discussion, see Farrar, 1990).
Assumption 3 is perhaps the model's most drastic simplification. Probably, neither Q1 nor Q2 are homogeneous and well-mixed pools, but comprise a set of biochemically and/or spatially distinct compartments. Further compartmentalization, however, did not improve goodness of fit of the model, although this may reflect a limited number of data points or the short time span of the experiment.
Assumption 4 was most likely true because fractionation during C and N transport and conversion are small compared to the 13C and 15N enrichments used.
Error Estimation and Statistics
The se associated with the determination of the total and proportional amount of labeled and nonlabeled C or N in the flux imported into the growth zone was estimated by Gaussian error propagation. Models were assessed by, and selection of alternative models based on, ANOVA. Ideally, partitioning the residual mean square into lack of fit and pure error terms would provide an objective basis for choosing between alternative models. In this case, however, lack of true time replicated 〈flab〉 values prevented this. Hence, the two-pool model was preferred over the one-pool model whenever it reduced residual mean square by at least one-half. Importantly, in this case, lack of time replication does not imply repeated measures because variables were estimated on different experimental units (i.e. plants) at different times.
In nonlinear regression, the se of a fitted parameter is of very limited value in assessing its significance. This is because the usually non-normal distribution of errors renders strongly asymmetric confidence intervals. Thus, se of fitted parameters in the one- and two-pool models (i.e. rate constants and ϕ) are given only for informative purposes. Rather, their usefulness resides in their use, along with the correlation matrix, to compute the se of predicted 〈flab〉 values and of derived quantities biologically meaningful, such as Q1 and Q2 sizes and half-times, and of the contribution of MX to IX [ϕ = MX/(TX + MX); see Ross, 1981 for a discussion]. Note that se of Q1 and Q2 sizes also involved se of total C and N import.
Acknowledgments
We thank Dr. Warren Williams (AgResearch, Palmerston North, New Zealand) for providing seeds of Paspalum dilatatum. The staff at the Lehrstuhl für Grünlandlehre (Technische Universität, Munich) provided invaluable assistance, particularly Rudi Schäufele and Wolfgang Feneis.
Footnotes
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Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.104.051375.
-
↵1 This work was supported by the Deutsche Forschungsgemeinschaft (SFB 607) and the Scottish Executive Environment and Rural Affairs Department. F.A.L. was partially supported by an award from the British Council and Fundación Antorchas (Argentina).
- Received August 9, 2004.
- Revised October 13, 2004.
- Accepted October 26, 2004.
- Published December 23, 2004.
LITERATURE CITED
